title: Retinal vessel detection and measurement for computer-aided medical diagnosis.
abstract: Since blood vessel detection and characteristic measurement for ocular retinal images is a fundamental problem in computer-aided medical diagnosis, automated algorithms/systems for vessel detection and measurement are always demanded. To support computer-aided diagnosis, an integrated approach/solution for vessel detection and diameter measurement is presented and validated. In the proposed approach, a Dempster-Shafer (D-S)-based edge detector is developed to obtain initial vessel edge information and an accurate vascular map for a retinal image. Then, the appropriate path and the centerline of a vessel of interest are identified automatically through graph search. Once the vessel path has been identified, the diameter of the vessel will be measured accordingly by the algorithm in real time. To achieve more accurate edge detection and diameter measurement, mixed Gaussian-matched filters are designed to refine the initial detection and measures. Other important medical indices of retinal vessels can also be calculated accordingly based on detection and measurement results. The efficiency of the proposed algorithm was validated by the retinal images obtained from different public databases. Experimental results show that the vessel detection rate of the algorithm is 100 % for large vessels and 89.9 % for small vessels, and the error rate on vessel diameter measurement is less than 5 %, which are all well within the acceptable range of deviation among the human graders.
authors.affiliation: TASC, Inc, 475 School Street SW, Washington, DC, 20024, USA, xiaokun@ieee.org.
authors.forename: Xiaokun
authors.initials: X
authors.lastname: Li
authors.forename: William G
authors.initials: WG
authors.lastname: Wee
mesh_terms: D000465:Algorithms; D001837:Body Weights and Measures / Q000379:methods* / Q000706:statistics & numerical data*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012171:Retinal Vessels / Q000033:anatomy & histology*